Review the Postsecondary Data Partnership course-level analysis-ready file. Follow Prakesh as he uses the course-level analysis-ready file to understand which gateway English and Math courses students took, explores if retention rates differ based on which gateway course was taken, and looks at the lowest course type retention to see if there is an equity gap by race/ethnicity.
Elise is our institution’s provost.
A few weeks ago, Elise shared information with faculty teaching math and English gateway courses that students who successfully completed those courses in their first year of college were more likely to retain than those who do not complete those courses in their first year of college.
Since then, she’s heard from the chairs of both departments asking for additional information.
Specifically, they would like to know:
- Which math and English gateway courses did students take?
- How do first to second year retention rates vary by different gateway math and English courses?
And, for the math and English gateway course with the lowest retention rate, is there an equity gap by race or ethnicity?
Elise forwards their request to Prakesh, the Director of Institutional Effectiveness.
Prakesh knows that one of the PDP Analysis ready files is likely the best source to answer these questions.
Then, Prakesh reviews the data dictionary for the Course-Level Analysis-Ready file in the Knowledge Base and finds that, using those student-level granular data, he can easily fulfill this data request.
Prakesh accesses the PDP FTP Secure site, downloads the file, and begins familiarizing himself with the dataset.
The first five fields are student identifying information.
The next six fields include Student ID, age, race, ethnicity, and gender.
The next four fields give information about the student cohort and the term they entered the institution. It also gives information on the academic year and term in which the student took the associated course.
The next six fields provide information on the student’s courses like course prefix, course number, section, name, CIP code where CIP stands for classification of instructional programs, and the course type like college-level undergraduate course or college developmental course.
The next two columns code the course as a math or English gateway course or a co-requisite developmental course.
The next two columns give the course begin and end dates.
The next four columns provide additional course information like the grade earned by the student, number of credits the student attempted, number of credits the student earned, and the delivery method of the course like face-to-face, online, hybrid.
The next three columns indicate if the course is part of the institution’s core or general education, the core competency the course is aligned with, and whether the student successfully met that core competency.
And the last field is provided by the National Student Clearinghouse and indicates if there is an enrollment record for that student at another institution at the same time.
Because these are course-level data, there are multiple rows per student, allowing for multiple points of comparison, including with a term or across terms. For example, if Prakesh wanted to look at a student named Olivia's course activity across multiple terms, he would notice that she took several psychology classes in the fall, Spring, and Summer terms.
Alternatively, if Prakesh wanted to look at a student named Liam's course activity for a single term, he would notice that he took classes like Principles of Accounting and Introduction to Geology in the spring term.
Now that Prakesh is familiar with the dataset, he’s ready to use it to answer the questions posed by the department chairs.
The first question is, “Which math and English gateway courses did students take?”
To answer this question, Prakesh filters to the most recent cohort.
Then, he creates a new variable which concatenated Course Prefix with Course Number. Using that new variable in combination with the math and English gateway indicator, he finds that first-year students in the current cohort took seven gateway math courses, like Basic Statistics and Precalculus, and four English gateway courses, like English Composition and Business Writing.
The second question is, “How do retention rates vary by different gateway math and English courses?”
For this question, Prakesh realizes that he will need to merge the Cohort Analysis-Ready File with the Course-Level Analysis-Ready File using Student ID to know whether students retained in their second year of college.
Prakesh uses the same fields as before with the Retention field from the Cohort-Level Analysis-Ready file to calculate the retention rates by gateway course type.
He finds that students who took Basic Statistics had the highest retention rate at 79% while students who took Precalculus had the lowest retention rate at 60%.
He also finds that students who took Business Writing retained at the highest rate of 71% while students who took English Composition had the lowest retention rate at 60%.
The last question is, “For the math and English gateway course with the lowest retention rate, is there an equity gap by race or ethnicity?”
For the final question, Prakesh filters the data to the most recent cohort. He also filters the course prefix to the math and English Gateway course with the lowest retention rate.
Then, using the Race and Retention variables, he finds that:
White students who took Precalculus had a retention rate of 77% compared to 58% for Black or African American students for a 19-percentage point gap.
And, white students who took English Composition had a retention rate of 78% compared to 56% for Black or African American students for a 22-percentage point gap.
Prakesh drafts a report summarizing this information and emails it to Elise who shares it with the department chairs.
Based on these data,
- the math department is implementing faculty-led inquiry and action with their section level data to identify barriers impeding success for Black or African American students.
- while the English department is implementing a corequisite writing lab course and using the data generated to identify ways to close equity gaps.
In summary, the Course-Level Analysis-Ready file can be used to:
- Analyze the course level experience of students by section, grade or delivery modality alongside student demographics.
- Merge with other Analysis-Ready files or institutional data to construct a powerful student success research dataset.
- Understand the impact of courses on student success.